Online- und Offline-Prozessierung von biologischen Zellbildern auf FPGAs und GPUs

Wenn Bilder von einem Mikroskop mit hohem Datendurchsatz aufgenommen werden, müssen sie wegen der großen Bildmenge in einer automatischen Analyse prozessiert werden. Es gibt zwei Ansätze: die Offlineprozessierung, die Verarbeitung der Bilder auf einem Cluster, und die Onlineprozessierung, die Verarbeitung des Pixelstroms direkt von den Sensoren. Für die Bewältigung der Bilddaten in der Offlineprozessierung setzt diese Arbeit auf Grafikkarten und demonstriert eine Implementierung der Haralick-Bildmerkmalerkennung in CUDA. Dabei wird der Algorithmus um den Faktor 1000, gegenüber einer CPU-Lösung, beschleunigt. Dies ermöglicht den Biologen weitere Tests und einen schnelleren Erkenntnisgewinn. Die Onlineprozessierung setzt auf FPGAs, die sich mit den Sensoren elektrisch verbinden lassen. Dabei soll sich der Algorithmus dem Bedarf der Biologen entsprechend verändern lassen. Diese Arbeit zeigt die Entwicklung eines OpenCL-FPGA-Kompilierer-Prototyps. Die Biologen können Algorithmen in OpenCL schreiben und in ein Hardwaredesign für den FPGA übersetzen, was in einer Hardwarebeschreibungssprache für sie zu komplex wäre. Neben der Einfachheit hat die parallele Sprache OpenCL den Vorteil der Portierbarkeit auf andere Architekturen. Falls der FPGA-Kompilierer wegen existierender Einschränkungen den Algorithmus nicht übersetzen kann, lässt sich das OpenCL-Programm auch für die GPUs in der Offlineprozessierung übersetzen

Characterizing Protein Interactions Employing a Genome-Wide siRNA Cellular Phenotyping Screen

<div><p>Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted <i>de novo</i> unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.</p></div

Workflow.

<p>Images of a genome-wide cellular RNAi knockdown screen (the screening data was derived from the Mitocheck project, <a href="http://www.mitocheck.org" target="_blank">www.mitocheck.org</a>) were segmented and their features extracted to compile pairwise phenotype descriptors for a large set of gene pairs. These descriptors were used to train a machine learning system to discriminate activating and inhibiting PPIs taken from a reference. The performance was evaluated using cross-validation. The trained SVM models were used to predict the effects of uncharacterized PPIs. In addition, the SVM models were used to estimate similarity of the effects of proteins for all combinations of protein pairs in the network. Subsequently, this Effect Similarity Rate (ESR) was exemplarily used for clustering of functionally related protein sub-networks.</p

a) Receiver Operating Characteristics curves for the predictions of activation.

<p>Cross-validation results for all pathways combined (AUC  = 0.75, dashed line) and when training and validation was done for each set of pathways separately (AUC  = 0.82, solid line). <b>b</b>) Histogram of the votes for activating PPIs (green) and inhibiting PPIs (blue) when training and validation was done for each set of major signaling pathways separately. The thresholds for 80% confidence were set at 920 and 88 votes for activation and inhibition, respectively (dashed lines).</p

Characterization of phenotypic similarity by linear discrimination.

<p>(<b>a-c</b>) Images of cells in which <i>sfrp1, dvl2</i> or <i>fzd7</i> were knocked down, respectively. (<b>d</b>) First two principal components (PC 1 and PC 2) of the features for cells with knockdown of <i>sfrp1</i> and <i>dvl2</i>. (<b>e</b>) First two principal components of the features for cells with knockdown of <i>dvl2</i> and <i>fzd7</i>. Dotted lines sketch a linear separation.</p

Clustering of the chemokine receptors and their interactors.

<p>Clustering of the chemokine receptors and their interactors.</p

Pairs of Pfam domain sets showing significant^* enrichment of predicted interactions.

<p>^*p≤0.1 only; t≥1 for both pos/neg - no zeroes.</p><p>Pairs of Pfam domain sets showing significant^{<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003814#nt101" target="_blank">*</a>} enrichment of predicted interactions.</p